This project turns edge devices such as Raspberry Pi 3 into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network.

At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. It not only saves costs of data transmission and storage but also makes devices able to respond according to the events shown in the images or videos without connecting to the cloud.

Figure 1: BerryNet architecture

Figure 1 shows the software architecture of the project, we use Node.js/Python, MQTT and an AI engine to analyze images or video frames with deep learning. So far, there are two default types of AI engines, the classification engine (with Inception v3 [1] model) and the object detection engine (with TinyYOLO [2] model or MobileNet SSD [3] model). Figure 2 shows the differences between classification and object detection.

Figure 2: Classification vs detection

One of the application of this intelligent gateway is to use the camera to monitor the place you care about. For example, Figure 3 shows the analyzed results from the camera hosted in the DT42 office. The frames were captured by the IP camera and they were submitted into the AI engine. The output from the AI engine will be shown in the dashboard. We are working on the Email and IM notification so you can get a notification when there is a dog coming into the meeting area with the next release.

Figure 3: Object detection result example

To bring easy and flexible edge AI experience to user, we keep expending support of the AI engines and the reference HWs.

Figure 4: Reference hardwares

Installation

You can install BerryNet by using pre-built image or from source. Please refer to the Wiki page for the details.

We are pushing BerryNet into Debian repository, so you will be able to install by only typing one command in the future.